1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
2/3/2025 Comida 102058 Tami Supermercado
3/3/2025 Comida 9370 Andrés NA
9/3/2025 Comida 61916 Tami Supermercado
11/3/2025 Comida 27021 Andrés NA
11/3/2025 Enceres 13190 Tami 40 rollos confort
15/3/2025 Comida 78061 Tami Supermercado
17/3/2025 Electricidad 52458 Andrés NA
17/3/2025 VTR 22000 Andrés NA
21/3/2025 Agua 19562 Andrés NA
22/3/2025 Comida 76766 Tami Supermercado
21/3/2025 Diosi 18500 Andrés antiparasitario
27/3/2025 Gas 82450 Andrés NA
26/3/2025 Comida 4000 Andrés avena multigrano y chucrut
29/3/2025 Comida 70591 Tami Supermercado
3/4/2025 Gas 83300 Andrés NA
4/4/2025 Agua 20807 Andrés NA
6/4/2025 Comida 52655 Tami Supermercado
12/4/2025 Comida 72108 Tami Supermercado
16/4/2025 VTR 21990 Andrés NA
22/4/2025 Comida 107881 Tami Supermercado
26/4/2025 Comida 55874 Tami Supermercado
28/4/2025 Comida 13050 Tami Cervezas MUT
29/4/2025 Electricidad 52507 Andrés enel
29/4/2025 Diosi 11990 Andrés arena 7kg superzoo
3/5/2025 Agua 17072 Andrés aguas andina
13/5/2025 VTR 22000 Andrés NA
17/5/2025 Electricidad 52404 Andrés NA
13/6/2025 VTR 22000 Andrés NA
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  theme_custom() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))

tsData_gastos = decompose(tsData_gastos)

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7  # Weekly data
num_periods <- length(tsData_gastos$x)  # Total number of periods in your time series

# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)

# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
  day = dates,
  Actual = as.numeric(tsData_gastos$x),
  Seasonal = as.numeric(tsData_gastos$seasonal),
  Trend = as.numeric(tsData_gastos$trend),
  Random = as.numeric(tsData_gastos$random)
)

tsData_gastos_long <- tsData_gastos_df %>%
  pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"), 
               names_to = "Component", values_to = "Value")

# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
  facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  theme(strip.text = element_text(size = 12))

#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 9.8533e+08   2    5.0572 0.0066 ** 
## lag_depvar    2.6311e+11   1 2700.8304 <2e-16 ***
## Residuals     8.1637e+10 838                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff       lwr      upr     p adj
## 1-0  7228.838 -1815.288 16272.96 0.1461049
## 2-0 31342.464 23204.768 39480.16 0.0000000
## 2-1 24113.626 19404.275 28822.98 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
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## 29   28706.00             0   28640.00
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## 711  88353.29             2  114267.57
## 712  88750.86             2   88353.29
## 713  78835.71             2   88750.86
## 714  75519.14             2   78835.71
## 715  73202.86             2   75519.14
## 716  53433.29             2   73202.86
## 717  48165.71             2   53433.29
## 718  52163.14             2   48165.71
## 719  49306.86             2   52163.14
## 720  36846.86             2   49306.86
## 721  43220.57             2   36846.86
## 722  38952.29             2   43220.57
## 723  41522.29             2   38952.29
## 724  39090.00             2   41522.29
## 725  28452.57             2   39090.00
## 726  32975.00             2   28452.57
## 727  33690.71             2   32975.00
## 728  26405.29             2   33690.71
## 729  47087.43             2   26405.29
## 730  49660.29             2   47087.43
## 731  47409.71             2   49660.29
## 732  53881.71             2   47409.71
## 733  45189.57             2   53881.71
## 734  45503.86             2   45189.57
## 735  54640.14             2   45503.86
## 736  39131.29             2   54640.14
## 737  35024.14             2   39131.29
## 738  44755.43             2   35024.14
## 739  41063.29             2   44755.43
## 740  42783.29             2   41063.29
## 741  45952.57             2   42783.29
## 742  44937.43             2   45952.57
## 743  40838.43             2   44937.43
## 744  48838.43             2   40838.43
## 745  43139.14             2   48838.43
## 746  67134.29             2   43139.14
## 747  73224.29             2   67134.29
## 748  68770.71             2   73224.29
## 749  59539.29             2   68770.71
## 750  82179.86             2   59539.29
## 751  74252.14             2   82179.86
## 752  73015.00             2   74252.14
## 753  56116.43             2   73015.00
## 754 111885.00             2   56116.43
## 755 131425.14             2  111885.00
## 756 136678.00             2  131425.14
## 757 115531.29             2  136678.00
## 758 118310.86             2  115531.29
## 759 117449.43             2  118310.86
## 760 115193.57             2  117449.43
## 761  61025.43             2  115193.57
## 762  43913.86             2   61025.43
## 763  46099.29             2   43913.86
## 764  44524.86             2   46099.29
## 765  42208.71             2   44524.86
## 766 166486.57             2   42208.71
## 767 171565.29             2  166486.57
## 768 200415.71             2  171565.29
## 769 204498.14             2  200415.71
## 770 197558.86             2  204498.14
## 771 195266.57             2  197558.86
## 772 203144.29             2  195266.57
## 773  85493.71             2  203144.29
## 774  74721.57             2   85493.71
## 775  36232.14             2   74721.57
## 776  40161.71             2   36232.14
## 777  40629.86             2   40161.71
## 778  45663.71             2   40629.86
## 779  39252.29             2   45663.71
## 780  39618.57             2   39252.29
## 781  39438.43             2   39618.57
## 782  44650.71             2   39438.43
## 783  38626.71             2   44650.71
## 784  38280.43             2   38626.71
## 785  44134.14             2   38280.43
## 786  47596.43             2   44134.14
## 787  45598.43             2   47596.43
## 788  42564.29             2   45598.43
## 789  45699.14             2   42564.29
## 790  49553.86             2   45699.14
## 791  50018.43             2   49553.86
## 792  43772.86             2   50018.43
## 793  39235.43             2   43772.86
## 794  39905.00             2   39235.43
## 795  40374.43             2   39905.00
## 796  34230.57             2   40374.43
## 797  34324.14             2   34230.57
## 798  33491.57             2   34324.14
## 799  33366.43             2   33491.57
## 800  46646.86             2   33366.43
## 801  49770.86             2   46646.86
## 802  57339.86             2   49770.86
## 803  59799.14             2   57339.86
## 804  53577.14             2   59799.14
## 805  61775.29             2   53577.14
## 806  70627.86             2   61775.29
## 807  57888.43             2   70627.86
## 808  49960.71             2   57888.43
## 809  42923.71             2   49960.71
## 810  47284.86             2   42923.71
## 811  52284.86             2   47284.86
## 812  50191.00             2   52284.86
## 813  36465.86             2   50191.00
## 814  34525.14             2   36465.86
## 815  43199.14             2   34525.14
## 816  52757.43             2   43199.14
## 817  43200.86             2   52757.43
## 818  36772.29             2   43200.86
## 819  29568.00             2   36772.29
## 820  42362.00             2   29568.00
## 821  42566.29             2   42362.00
## 822  39596.00             2   42566.29
## 823  32925.00             2   39596.00
## 824  43416.57             2   32925.00
## 825  52624.86             2   43416.57
## 826  57733.71             2   52624.86
## 827  54120.57             2   57733.71
## 828  53353.43             2   54120.57
## 829  56286.86             2   53353.43
## 830  60626.86             2   56286.86
## 831  61375.29             2   60626.86
## 832  53710.86             2   61375.29
## 833  55795.57             2   53710.86
## 834  55130.14             2   55795.57
## 835  57700.14             2   55130.14
## 836  61333.14             2   57700.14
## 837  59230.71             2   61333.14
## 838  49195.00             2   59230.71
## 839  55436.43             2   49195.00
## 840  50353.14             2   55436.43
## 841  43194.86             2   50353.14
## 842  47539.71             2   43194.86
## 843  35271.00             2   47539.71
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   686 53576.73 22058.674
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00  64437.14  64297.14  64628.57  51413.14  52969.43
## [666]  54135.29  48799.43  41907.86  45382.00  42633.29  46624.71  44051.86
## [673]  35852.86  29737.71  29734.86  32881.71  38298.57  40886.14  38601.86
## [680]  38628.86  39142.57  32666.14  39911.57  39336.29  39678.86  41963.14
## [687]  54220.57  63901.86  73116.00  60863.86  56293.86  52725.00  58625.00
## [694]  47513.00  40300.14  33312.43  29556.71  27816.71  34120.29  32132.57
## [701]  32902.57  39694.14  72501.29  79551.14  99637.71  95424.29  98395.14
## [708] 115594.71 114267.57  88353.29  88750.86  78835.71  75519.14  73202.86
## [715]  53433.29  48165.71  52163.14  49306.86  36846.86  43220.57  38952.29
## [722]  41522.29  39090.00  28452.57  32975.00  33690.71  26405.29  47087.43
## [729]  49660.29  47409.71  53881.71  45189.57  45503.86  54640.14  39131.29
## [736]  35024.14  44755.43  41063.29  42783.29  45952.57  44937.43  40838.43
## [743]  48838.43  43139.14  67134.29  73224.29  68770.71  59539.29  82179.86
## [750]  74252.14  73015.00  56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57  61025.43  43913.86  46099.29  44524.86
## [764]  42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29  85493.71  74721.57  36232.14  40161.71  40629.86  45663.71
## [778]  39252.29  39618.57  39438.43  44650.71  38626.71  38280.43  44134.14
## [785]  47596.43  45598.43  42564.29  45699.14  49553.86  50018.43  43772.86
## [792]  39235.43  39905.00  40374.43  34230.57  34324.14  33491.57  33366.43
## [799]  46646.86  49770.86  57339.86  59799.14  53577.14  61775.29  70627.86
## [806]  57888.43  49960.71  42923.71  47284.86  52284.86  50191.00  36465.86
## [813]  34525.14  43199.14  52757.43  43200.86  36772.29  29568.00  42362.00
## [820]  42566.29  39596.00  32925.00  43416.57  52624.86  57733.71  54120.57
## [827]  53353.43  56286.86  60626.86  61375.29  53710.86  55795.57  55130.14
## [834]  57700.14  61333.14  59230.71  49195.00  55436.43  50353.14  43194.86
## [841]  47539.71  35271.00
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##             2             3             4             5             6 
##   2019.947747   4040.826857   -538.488471   2437.710411  -2970.455559 
##             7             8             9            10            11 
##    518.446613  -5656.288601  -1187.200519  -3965.463927   -416.675962 
##            12            13            14            15            16 
##  -4938.595890  -1607.516361   -897.854591    379.247892  -3241.458596 
##            17            18            19            20            21 
##   -376.073673  -2128.486855   6605.915719  -1529.219056  -1208.125594 
##            22            23            24            25            26 
##   1475.890499  -1186.788041    234.617340   1694.831899  -7102.625817 
##            27            28            29            30            31 
##    948.516757   8193.073990    417.191599    -14.925416  -2401.363918 
##            32            33            34            35            36 
##   1576.033665   4572.066707   1125.651912   2390.166770  -1869.542993 
##            37            38            39            40            41 
##   4607.013462   4303.218335  -2276.731517  -2981.838886  -1109.868781 
##            42            43            44            45            46 
## -10740.973967   7291.968168   2558.013477   1366.971958   8104.992001 
##            47            48            49            50            51 
##    684.346346   6527.056732   6711.587939  -5886.139165  -4797.524603 
##            52            53            54            55            56 
##  -5060.537936  -7928.209141   6131.775004  -4076.170118  -4893.106552 
##            57            58            59            60            61 
##   3857.869116    888.807889    -31.472340    142.773337  -4996.001263 
##            62            63            64            65            66 
##  18128.179579   3638.125572  -3649.048561   5923.607883   7341.367324 
##            67            68            69            70            71 
##  14635.151281   1687.076973 -13218.257927  -1308.031038   4642.270355 
##            72            73            74            75            76 
##  -4902.239695  -4405.075205 -10496.823519   2469.983064  -5397.348850 
##            77            78            79            80            81 
##   1067.301198  -6863.257311    551.310714  -2350.716064  -2689.791873 
##            82            83            84            85            86 
##  -3927.526358   -532.623797   2319.151141   3766.146173    479.005654 
##            87            88            89            90            91 
##   -482.866074    198.160573   4303.211892  -1162.973214   1151.153214 
##            92            93            94            95            96 
##  -2064.494103  -1043.810623    178.263627    275.335250  -7483.623518 
##            97            98            99           100           101 
##   2394.072963  -8600.993723  -2936.863366  -4035.191768  -1731.640747 
##           102           103           104           105           106 
##  -1256.142138   3186.081078  -2338.286532   2598.020683  -1155.534239 
##           107           108           109           110           111 
##    973.330807   2589.388183  -3153.040416  -4720.997284   -847.133718 
##           112           113           114           115           116 
##   1906.571053  11695.771363  -1243.718361   2667.905792   4261.653302 
##           117           118           119           120           121 
##   3500.604476  -1102.575143  -4718.239538  -3724.411769   2320.592808 
##           122           123           124           125           126 
##  -1732.433450   1341.176560   8858.652342    845.230552    128.688111 
##           127           128           129           130           131 
##  -2522.742410   2654.533921   7051.453483   1009.752271  -8501.934586 
##           132           133           134           135           136 
##   1749.279015   4135.182953  -3165.268628  -1419.993675   -853.769080 
##           137           138           139           140           141 
##  -3879.531695   1184.560261   -494.393906  -2912.461292   1719.997048 
##           142           143           144           145           146 
##  -1879.877280  -7827.702705   2042.984443  -3477.137150   2105.417511 
##           147           148           149           150           151 
##   -255.261108   1025.011842   -357.861603   1353.529220   1187.420289 
##           152           153           154           155           156 
##   3356.934028  -4862.382261  -1173.682818  -3234.791150   5958.491576 
##           157           158           159           160           161 
##   9746.605833  -3623.824664  -4969.535959   3413.882865      3.171332 
##           162           163           164           165           166 
##   2504.085903  -6104.052822  -6939.393623   3966.524669  17199.763351 
##           167           168           169           170           171 
##   3420.579070   -608.237726  -2655.145199  -1311.462811   3383.917432 
##           172           173           174           175           176 
##   -437.340024  -8284.310517   2660.103033   4119.278255    415.952632 
##           177           178           179           180           181 
##   8540.312700  -9464.861930  -3681.037851 -10951.703129 -11440.284094 
##           182           183           184           185           186 
##   1041.182907   9096.642777  -1635.284565   5723.295365   6343.579386 
##           187           188           189           190           191 
##  12938.909423   8197.225419  -4306.491495   2223.830229  10123.858177 
##           192           193           194           195           196 
##  -1899.753942  -2698.887828 -10532.025015  -6603.615936   1000.063932 
##           197           198           199           200           201 
##  -5467.897566 -10024.672294   5165.538188  -3291.995314  -1932.816397 
##           202           203           204           205           206 
##  -1023.006492   6275.664924   9653.507816    335.248354   2680.281476 
##           207           208           209           210           211 
##   2849.926529   5533.051472  12576.295536  -5958.597567 -11557.211029 
##           212           213           214           215           216 
##  -5910.999505 -10823.469670  -5297.278816   1311.008824 -13228.559747 
##           217           218           219           220           221 
##  16186.910348   7584.067755   1291.562330  26449.293098  12250.687781 
##           222           223           224           225           226 
##   7044.582643  13730.221378  -4224.138044  -2040.067117   3486.328909 
##           227           228           229           230           231 
##     69.775419   2461.841637   8723.392839   5545.414571  -2189.486015 
##           232           233           234           235           236 
##  -2101.659104   9159.962165 -11781.448186  -7537.703579  -8783.991473 
##           237           238           239           240           241 
## -10332.513891   2859.226683   1129.237859  -8521.856643  -9203.991827 
##           242           243           244           245           246 
##   8890.581632  -7983.404647   2278.238799 -10515.398562  -4256.336553 
##           247           248           249           250           251 
##   1222.817484    798.041451 -12525.157926   3447.979788   1858.140945 
##           252           253           254           255           256 
##   4001.145934   1915.156382  -1385.548572  10913.873968  20636.550266 
##           257           258           259           260           261 
##   2917.766738  -4554.609292   3834.784603  -1974.026187   3462.742540 
##           262           263           264           265           266 
##  -5129.927785 -11161.663775  -4977.811072   -763.213149  -5429.453862 
##           267           268           269           270           271 
##   8544.300229  -4531.085816   3944.792309  -2360.280460   4180.208713 
##           272           273           274           275           276 
##    449.408346   7041.510101  -1687.458289  11752.498062  -4880.353237 
##           277           278           279           280           281 
##   1438.714179   -661.293725   7564.568671  -5358.407129  -3018.779308 
##           282           283           284           285           286 
## -11540.412167  -2922.102295  18408.203854   7496.655320   2432.148779 
##           287           288           289           290           291 
##   -933.681925    605.471697   6098.622328   6571.536793 -19094.627748 
##           292           293           294           295           296 
## -11410.845260  -8363.002046   9444.496722   2828.266299  -1429.683293 
##           297           298           299           300           301 
##  27155.013575   9750.828822   4566.337125   9178.299948   2501.081565 
##           302           303           304           305           306 
##  -1385.466725   7555.673769 -24647.124881  -3811.674094   -437.237331 
##           307           308           309           310           311 
##  -7225.400648  -4206.459139   2710.543670  -9420.573503  -3431.382286 
##           312           313           314           315           316 
##  -8378.318071   1395.536267  -3328.316017   1876.809942  -4264.135101 
##           317           318           319           320           321 
##  27271.016545  -1001.363347   3017.278198  10548.475909   5278.622272 
##           322           323           324           325           326 
##  32059.635399   4706.198130 -21338.533963   1469.018189    791.431136 
##           327           328           329           330           331 
##  -6778.090532  -2019.469027 -33541.184351    749.016602  -2437.528493 
##           332           333           334           335           336 
##   -221.594944  -3297.439479   3963.821044   -575.236050  -7091.748387 
##           337           338           339           340           341 
##  -3235.858068  -2304.821801  -7790.132993   3760.904966  -1482.136581 
##           342           343           344           345           346 
##  -1850.071146  -1106.098781     62.014854    360.760908  -1746.579533 
##           347           348           349           350           351 
##  -9574.666105 -13312.627416   2243.337908  -4408.312920  -3737.939697 
##           352           353           354           355           356 
##  -6056.323812   1684.774200   1300.858976   2654.081592  -3885.295611 
##           357           358           359           360           361 
##   -630.514874    556.455696   6882.453147    113.134542   -206.920470 
##           362           363           364           365           366 
##   2411.009128  -2934.468164  -1052.262183  -8915.908797  -4765.761308 
##           367           368           369           370           371 
##  -6337.307803  -5054.871690  -7345.100468   4942.297688    268.078784 
##           372           373           374           375           376 
##   7006.770502  -7784.435326  -2385.353794  -3506.622609  -2577.916434 
##           377           378           379           380           381 
## -12564.700991   1836.762942 -10718.220630   5643.378672   9248.011399 
##           382           383           384           385           386 
##   2990.365195  -2553.774492   1453.882027   6581.579934  11216.292474 
##           387           388           389           390           391 
##  -6046.258682  -5586.078254   -361.555854   8357.761629   1572.908512 
##           392           393           394           395           396 
##  10972.788741 -10172.509457   2522.073156    450.674663    299.972857 
##           397           398           399           400           401 
##   -915.721073   -819.425868 -14738.533836   8339.399981  -1395.718963 
##           402           403           404           405           406 
##  -1578.964770   6783.431487  -8158.876797  -1485.410525  -2711.717427 
##           407           408           409           410           411 
##  -5985.950443  -2999.137234  -4045.564245  -8869.113574   6052.902236 
##           412           413           414           415           416 
##   1525.307951  -7501.817619  -7793.738090  14145.731951   3666.413887 
##           417           418           419           420           421 
##   4317.411805  -8235.410789  -4908.951432  -2745.716307   2684.834324 
##           422           423           424           425           426 
## -14161.407846  -2883.397727  -9184.865424   2959.022555   6899.026529 
##           427           428           429           430           431 
##   6456.292897  -4142.955515  -4262.123328  -4849.003087  -1900.286070 
##           432           433           434           435           436 
##  -5820.548308  -6716.898235  -6019.324854  -1447.367359   -907.291706 
##           437           438           439           440           441 
##  -5042.102834   2525.230977   4760.051590  -5167.179897  -2254.093843 
##           442           443           444           445           446 
##   1481.806535  -3946.483497   2735.902310  -6697.373788 -12207.466545 
##           447           448           449           450           451 
##  -4565.023566   9599.520904  -2129.049673   4659.038875  -5991.391597 
##           452           453           454           455           456 
##  -1224.515214    280.750976   2915.802633 -12396.584553   3286.907693 
##           457           458           459           460           461 
##  -6804.843252   6439.770113   2895.690946   2373.449322  -3992.752682 
##           462           463           464           465           466 
##   1959.469704   -152.066733   1646.273446   -677.004080   3195.709504 
##           467           468           469           470           471 
##  -2808.468841   5647.499963  -7122.856832  -3113.553279  -2341.550548 
##           472           473           474           475           476 
##  -4791.238705   2886.532075   7672.042537  -6175.988768   1350.417968 
##           477           478           479           480           481 
##  -6319.602753  -2960.881055   1904.281042 -13049.015619  -9827.282479 
##           482           483           484           485           486 
##  -1244.781891    -28.143129  -1020.799572  -1405.994425  -9652.463164 
##           487           488           489           490           491 
##  11055.318460   6141.768576   7296.665466  -5592.202470   5232.508049 
##           492           493           494           495           496 
##   9135.418354   5860.229633 -13687.290166 -10721.393031  -3554.328162 
##           497           498           499           500           501 
##  -1208.061919   -625.752547  -7728.979247    534.044882   4202.749105 
##           502           503           504           505           506 
##   5402.195300    529.440195    -54.105652  -7374.737603    461.287878 
##           507           508           509           510           511 
##  -5161.959985   1735.599513  -1404.297746  -8263.922547   -680.900134 
##           512           513           514           515           516 
##  -2756.978179   -665.894596   1250.071606  -9588.181041  -7827.968558 
##           517           518           519           520           521 
##  24244.330470   9681.998388   5698.068760  -5536.727395   2621.756971 
##           522           523           524           525           526 
##  16834.151433  11226.889057 -24430.876162  -5238.211232  -3889.349225 
##           527           528           529           530           531 
##   4431.371302   -515.471102 -11259.212477   4277.877922  13776.529135 
##           532           533           534           535           536 
##  -5164.256782   4207.839569   5373.235095  -1991.150010  -4730.636243 
##           537           538           539           540           541 
##  -7242.602625  -2239.547246   8191.678362    -36.061458  -8303.470791 
##           542           543           544           545           546 
##   1686.656992   -736.772413    230.953234 -11168.163106 -11157.682872 
##           547           548           549           550           551 
##   1982.028741   6929.253774  -1424.758735    731.256629  -7833.100580 
##           552           553           554           555           556 
##   8478.610876    782.680414 -12073.907497   9075.465497   8529.795194 
##           557           558           559           560           561 
##    -64.809750   4689.208631  -3756.271548  13942.227011  21279.247907 
##           562           563           564           565           566 
##  -6763.130212  -9940.227828   6562.186581     -6.862921   3228.313084 
##           567           568           569           570           571 
##  -7609.473528 -17503.206293   6540.734727   6287.842770   1733.147589 
##           572           573           574           575           576 
##   2925.660418   1589.590717  -2347.414873  14547.463486  -9871.585943 
##           577           578           579           580           581 
##  -6424.271105   8558.527027   2674.107368  -6736.501134   7347.339764 
##           582           583           584           585           586 
##  -3987.255274  -2944.014273  15547.776682 -14711.341901   8276.279009 
##           587           588           589           590           591 
##   -111.379672  -6389.795140   -903.676379    107.581752 -10796.766383 
##           592           593           594           595           596 
##   1694.660400  -7255.282043   2981.909303   8765.424600  -7637.236889 
##           597           598           599           600           601 
##   5742.660866   2602.166387   6712.669389  -3357.777878   5996.941687 
##           602           603           604           605           606 
##  -8471.525936   2116.989967   1124.586482   2990.076069   1337.683295 
##           607           608           609           610           611 
##    237.581904  -5968.336983   7941.553544  -1344.996206  -2726.942390 
##           612           613           614           615           616 
##  -3593.130923  -8352.987024  11864.678260   4797.735318  -9468.703473 
##           617           618           619           620           621 
##  11508.957809   5891.528010  -5748.450284  26210.598959 -13060.758651 
##           622           623           624           625           626 
##  -6955.767612   3012.274794  -4311.122451 -10725.279994  11202.312108 
##           627           628           629           630           631 
## -21769.523801  -2474.408205   8618.728283  11044.819036  -1682.417549 
##           632           633           634           635           636 
##  33161.548317  -6818.564425   5520.243458   5192.503823  -2482.748095 
##           637           638           639           640           641 
##  -5540.575025  -2108.455536 -12586.513249  -2352.283813  -1988.508042 
##           642           643           644           645           646 
##  -2616.870033  -2947.383330   1733.177753   4340.499992  16858.144082 
##           647           648           649           650           651 
##  18390.627838    665.028012   4577.903173  10394.903796  19910.618936 
##           652           653           654           655           656 
##    458.223193 -28330.229944  -1500.221218  -2436.995375   1737.520810 
##           657           658           659           660           661 
##  -3321.820799 -10735.602755   1586.589945   4143.599875  -1101.379512 
##           662           663           664           665           666 
##  12942.025896   1233.619859   1687.417857 -11817.701603   1289.758536 
##           667           668           669           670           671 
##   1095.317173  -5259.577615  -7487.250614   2010.589712  -3774.759360 
##           672           673           674           675           676 
##   2619.230580  -3442.403895  -9392.553510  -8341.218292  -2999.028818 
##           677           678           679           680           681 
##    150.325660   2816.617768    669.491313  -3876.506063  -1852.886537 
##           682           683           684           685           686 
##  -1362.772070  -8288.221517   4618.041712  -2290.236440  -1444.826561 
##           687           688           689           690           691 
##    540.028658  10800.837704   9768.305500  10520.352759  -9785.571385 
##           692           693           694           695           696 
##  -3646.373536  -3220.742961   5798.678489 -10470.318784  -7970.539707 
##           697           698           699           700           701 
##  -8653.731176  -6301.712662  -4758.965488   3065.483103  -4431.977059 
##           702           703           704           705           706 
##  -1924.580932   4193.958649  31064.810771   9439.016007  23363.537595 
##           707           708           709           710           711 
##   1593.093077   8246.770500  22849.612745   6488.886336 -18265.387159 
##           712           713           714           715           716 
##   4783.014907  -5479.632149   -129.701090    452.919944 -17292.061804 
##           717           718           719           720           721 
##  -5279.696632   3321.944057  -3028.363393 -12991.777352   4272.815920 
##           722           723           724           725           726 
##  -5566.525250    734.243867  -3944.394897 -12455.841978   1364.415430 
##           727           728           729           730           731 
##  -1872.777370  -9783.788420  17266.309569   1761.581234  -2737.840579 
##           732           733           734           735           736 
##   5701.310391  -8647.796255   -735.992403   8125.586435 -15368.999609 
##           737           738           739           740           741 
##  -5920.357043   7400.848080  -4797.093742    150.087724   1815.977623 
##           742           743           744           745           746 
##  -1969.334351  -5181.031139   6401.770864  -6290.053525  22686.648804 
##           747           748           749           750           751 
##   7803.278248  -1973.363247  -7312.070511  23397.391081  -4319.707123 
##           752           753           754           755           756 
##   1372.506114 -14444.719155  56094.341558  26888.997841  15062.454403 
##           757           758           759           760           761 
## -10675.610724  10587.612892   7296.651731   5793.741164 -46402.630654 
##           762           763           764           765           766 
## -16167.597844    974.496354  -2510.143944  -3450.130216 122852.191740 
##           767           768           769           770           771 
##  19303.690704  43714.981018  22580.192391  12072.589217  15845.706470 
##           772           773           774           775           776 
##  25727.032819 -98809.191336  -6746.812831 -35820.663209   1751.260455 
##           777           778           779           780           781 
##  -1215.306712   3409.362051  -7401.996615  -1431.690619  -1931.991854 
##           782           783           784           785           786 
##   3437.750847  -7142.137834  -2223.041925   3933.349392   2279.094664 
##           787           788           789           790           791 
##  -2745.176182  -4032.932505   1753.969798   2868.607857    -36.100555 
##           792           793           794           795           796 
##  -6687.738694  -5766.117295  -1130.527770  -1246.349712  -7800.519035 
##           797           798           799           800           801 
##  -2336.802771  -3251.161928  -2648.581296  10741.230559   2257.241757 
##           802           803           804           805           806 
##   7095.655404   2939.125464  -5432.455844   8204.133968   9890.976522 
##           807           808           809           810           811 
## -10586.195561  -7378.791473  -7486.435379   3025.519310   4213.586798 
##           812           813           814           815           816 
##  -2250.607017 -14145.577743  -4089.592997   6280.721966   8257.347621 
##           817           818           819           820           821 
##  -9653.809123  -7729.293637  -9314.575058   9776.455752  -1202.076010 
##           822           823           824           825           826 
##  -4350.921194  -8425.691477   7896.783139   7934.728981   4994.924375 
##           827           828           829           830           831 
##  -3083.703627   -692.716338   2911.246745   4687.232653   1642.208993 
##           832           833           834           835           836 
##  -6676.396545   2107.544393   -380.064838   2771.564540   4158.211491 
##           837           838           839           840           841 
##  -1119.703707  -9317.753855   5695.564751  -4843.149798  -7558.301518 
##           842           843 
##   3043.379339 -13023.032649 
## 
## $fitted.values
##         2         3         4         5         6         7         8         9 
##  17249.34  20098.17  24354.63  24072.43  26427.17  23758.27  24475.00  19704.34 
##        10        11        12        13        14        15        16        17 
##  19440.75  16781.96  17559.88  14287.37  14338.57  15003.61  16701.17  15020.22 
##        18        19        20        21        22        23        24        25 
##  16055.49  15428.66  22515.22  21598.70  21078.25  22969.36  22294.95  22947.88 
##        26        27        28        29        30        31        32        33 
##  24794.91  18719.77  20446.93  28288.81  28346.50  28019.22  25647.25  27050.50 
##        34        35        36        37        38        39        40        41 
##  30895.78  31244.40  32654.40  30163.56  34139.78  37349.73  34404.12  31213.15 
##        42        43        44        45        46        47        48        49 
##  30060.26  20634.32  28157.42  30595.31  31685.15  38527.23  38021.51  42686.41 
##        50        51        52        53        54        55        56        57 
##  46925.14  39618.81  34184.11  29203.92  22344.37  28638.03  25216.68  21512.13 
##        58        59        60        61        62        63        64        65 
##  25923.05  27183.33  27480.51  27892.57  23761.11  40362.02  42207.05  37450.25 
##        66        67        68        69        70        71        72        73 
##  41659.63  46578.13  57252.49  55265.12  40499.75  38004.16  41023.81  35320.65 
##        74        75        76        77        78        79        80        81 
##  30770.25  21468.30  24671.63  20594.98  22682.26  17574.83  19591.43  18817.51 
##        82        83        84        85        86        87        88        89 
##  17844.67  15912.48  17190.99  20801.14  25221.42  26211.87  26236.84  26853.93 
##        90        91        92        93        94        95        96        97 
##  30981.40  29811.28  30811.21  28874.52  28073.88  28442.24  28849.05  22422.78 
##        98        99       100       101       102       103       104       105 
##  25439.57  18466.01  17321.48  15361.07  15661.00  16338.78  20814.00  19896.98 
##       106       107       108       109       110       111       112       113 
##  23410.11  23199.95  24877.04  27755.47  25252.14  21693.56  21969.14  24616.94 
##       114       115       116       117       118       119       120       121 
##  35487.72  33679.52  35518.06  38518.11  40475.15  38162.24  32980.27  29319.55 
##       122       123       124       125       126       127       128       129 
##  31403.58  29682.54  30864.78  38468.91  38111.17  37172.17  34033.89  35816.12 
##       130       131       132       133       134       135       136       137 
##  41217.10  40657.08  31853.72  33119.25  36310.84  32719.42  31105.77  30190.25 
##       138       139       140       141       142       143       144       145 
##  26745.30  28160.54  27930.03  25615.00  27640.59  26264.56  19863.02  22895.28 
##       146       147       148       149       150       151       152       153 
##  20720.73  23699.55  24239.85  25831.15  26013.33  27668.44  28969.92  32003.81 
##       154       155       156       157       158       159       160       161 
##  27471.40  26733.93  24287.79  30185.25  41644.25  39973.54  37336.97  42360.11 
##       162       163       164       165       166       167       168       169 
##  43769.49  47187.34  42650.68  37955.19  43383.52  59694.99  61908.38  60321.57 
##       170       171       172       173       174       175       176       177 
##  57145.46  55543.80  58247.91  57271.45  49559.18  52384.29  56129.05  56165.26 
##       178       179       180       181       182       183       184       185 
##  63298.15  53795.04  50544.13  41347.57  32882.10  36392.36  46501.57  45957.28 
##       186       187       188       189       190       191       192       193 
##  51913.42  57661.66  68450.77  73736.63  67427.74  67621.28  74695.61  70369.60 
##       194       195       196       197       198       199       200       201 
##  65889.88  55127.62  49154.36  50579.47  46171.67  38336.03  44764.42  42990.82 
##       202       203       204       205       206       207       208       209 
##  42628.58  43107.19  49905.06  58799.32  58428.72  60154.50  61811.23  65604.56 
##       210       211       212       213       214       215       216       217 
##  75076.45  67154.78  55337.14  49942.90  40934.14  37890.13  41005.56  31020.09 
##       218       219       220       221       222       223       224       225 
##  48003.22  55328.15  56230.56  79008.88  86508.13  88512.49  96108.14  87053.92 
##       226       227       228       229       230       231       232       233 
##  81048.96  80630.65  77278.73  76439.75  81179.44  82544.49  76976.80  72187.04 
##       234       235       236       237       238       239       240       241 
##  77843.88  64484.13  56516.13  48462.23  40069.06  44263.33  46417.29  39864.28 
##       242       243       244       245       246       247       248       249 
##  33540.28  43828.55  38072.19  42010.11  34269.62  32974.75  36632.10  39457.59 
##       250       251       252       253       254       255       256       257 
##  30281.88  36223.29  40026.85  45224.56  47944.41  47436.70  57743.45  75250.52 
##       258       259       260       261       262       263       264       265 
##  75065.47  68372.36  69855.03  66073.69  67520.64  61274.81  50543.38  46568.50 
##       266       267       268       269       270       271       272       273 
##  46778.03  42882.56  51691.66  47962.64  52111.71  50227.22  54296.88  54593.06 
##       274       275       276       277       278       279       280       281 
##  60613.89  58246.79  67925.21  61846.57  62056.72  60404.86  66150.98  59877.92 
##       282       283       284       285       286       287       288       289 
##  56439.84  45986.25  44382.08  61624.06  67157.28  67566.97  64983.10  64069.95 
##       290       291       292       293       294       295       296       297 
##  68073.18  71985.63  52971.42  43067.86  37075.50  47402.73  50646.40  49759.84 
##       298       299       300       301       302       303       304       305 
##  73969.89  79918.66  80586.70  85201.78  83399.32  78426.75  81895.55  56780.10 
##       306       307       308       309       310       311       312       313 
##  53039.09  52718.69  46505.32  43713.17  47318.57  39866.53  38587.89  33146.32 
##       314       315       316       317       318       319       320       321 
##  36933.03  36113.90  39947.56  37930.84  63731.93  61571.86  63196.38  71199.09 
##       322       323       324       325       326       327       328       329 
##  73587.79  99084.09  97460.82  73277.12  72074.28  70430.66  62377.75  59498.33 
##       330       331       332       333       334       335       336       337 
##  29429.41  33119.10  33558.88  35880.15  35220.61  40990.95  42067.18  37312.00 
##       338       339       340       341       342       343       344       345 
##  36525.96  36652.70  31968.95  37971.42  38635.21  38893.81  39770.13  41557.10 
##       346       347       348       349       350       351       352       353 
##  43380.15  43131.67  36072.20  26634.52  31982.31  30842.65  30432.47  28047.51 
##       354       355       356       357       358       359       360       361 
##  32729.14  36485.63  40951.87  39139.80  40400.83  42540.55  49940.15  50491.06 
##       362       363       364       365       366       367       368       369 
##  50692.85  53157.47  50639.41  50083.62  42724.48  39919.59  36094.30  33871.67 
##       370       371       372       373       374       375       376       377 
##  29927.13  37219.35  39507.66  47397.86  41365.93  40812.77  39349.20  38881.70 
##       378       379       380       381       382       383       384       385 
##  29743.95  34344.79  27392.34  35616.56  45955.78  49523.35  47795.69  49788.56 
##       386       387       388       389       390       391       392       393 
##  56012.42  65503.54  58710.79  53175.70  52904.24  60288.23  60811.93  69485.80 
##       394       395       396       397       398       399       400       401 
##  58584.93  60152.75  59712.60  59196.15  57682.14  56442.96  43193.60  51784.43 
##       402       403       404       405       406       407       408       409 
##  50784.25  49749.85  56155.02  48692.98  48003.72  46329.38  42003.99  40833.99 
##       410       411       412       413       414       415       416       417 
##  38896.69  32987.24  40864.83  43792.96  38462.02  33547.27  48428.01  52275.16 
##       418       419       420       421       422       423       424       425 
##  56206.84  48671.38  44992.43  43667.59  47256.26  35668.25  35397.29  29652.55 
##       426       427       428       429       430       431       432       433 
##  35245.83  43578.56  50474.96  47238.41  44305.29  41228.57  41116.69  37592.33 
##       434       435       436       437       438       439       440       441 
##  33728.32  30960.65  32537.72  34388.25  32391.63  37260.81  43470.18  40220.52 
##       442       443       444       445       446       447       448       449 
##  39926.34  42934.63  40819.38  44811.37  40055.32  31082.02  29918.76  41282.76 
##       450       451       452       453       454       455       456       457 
##  40964.10  46618.82  42252.23  42602.11  44223.63  47944.16  37812.09  42664.41 
##       458       459       460       461       462       463       464       465 
##  38084.80  45658.59  49180.84  51803.04  48530.53  50872.78  51074.44  52822.58 
##       466       467       468       469       470       471       472       473 
##  52319.86  55265.47  52592.07  57646.43  50902.12  48511.55  47096.81  43719.04 
##       474       475       476       477       478       479       480       481 
##  47477.53  54945.56  49369.01  51073.32  45858.88  44236.86  47071.59  36479.14 
##       482       483       484       485       486       487       488       489 
##  30036.64  31907.14  34605.51  36096.42  37062.89  30699.68  43237.80  49902.19 
##       490       491       492       493       494       495       496       497 
##  56736.77  51444.92  56281.01  63919.48  67733.29  53980.96  44552.90  42576.63 
##       498       499       500       501       502       503       504       505 
##  42900.04  43691.69  38174.96  40575.39  45880.23  51565.42  52275.53  52386.17 
##       506       507       508       509       510       511       512       513 
##  46084.14  47424.96  43681.83  46439.01  46104.49  39816.33  40948.12  40122.75 
##       514       515       516       517       518       519       520       521 
##  41229.07  43870.75  36706.40  31982.81  55887.43  64053.22  67708.44  61083.39 
##       522       523       524       525       526       527       528       529 
##  62423.71  76017.83  82998.88  57933.50  52800.35  49492.63  53874.33  53380.36 
##       530       531       532       533       534       535       536       537 
##  43557.84  48552.76  61221.11  55738.59  59138.34  63128.58  60179.35  55207.03 
##       538       539       540       541       542       543       544       545 
##  48665.26  47320.32  55262.35  55012.61  47568.06  49793.06  49619.62  50313.88 
##       546       547       548       549       550       551       552       553 
##  40957.11  32787.83  37132.32  45253.90  45050.74  46757.67  40763.82  49782.32 
##       554       555       556       557       558       559       560       561 
##  50938.34  40711.25  50258.06  58125.67  57490.22  61090.13  56854.77  68622.47 
##       562       563       564       565       566       567       568       569 
##  85321.27  75406.23  63962.81  68384.72  66507.97  67695.33  59260.21  43239.55 
##       570       571       572       573       574       575       576       577 
##  50252.44  56161.14  57344.63  59421.41  60068.84  57193.54  69447.59  58814.56 
##       578       579       580       581       582       583       584       585 
##  52533.76  60139.89  61644.79  54734.66  61004.97  56578.44  53621.22  67199.48 
##       586       587       588       589       590       591       592       593 
##  52619.29  59967.95  59059.80  52778.25  52082.99  52359.19  43069.48  45868.00 
##       594       595       596       597       598       599       600       601 
##  40491.23  44739.58  53508.09  46835.34  52697.83  55077.04  60749.49  56905.34 
##       602       603       604       605       606       607       608       609 
##  61721.95  53285.58  55166.70  55943.50  58253.03  58827.42  58367.91  52541.88 
##       610       611       612       613       614       615       616       617 
##  59607.71  57666.66  54762.13  51466.27  44425.04  55942.12  59831.85  50761.90 
##       618       619       620       621       622       623       624       625 
##  61170.04  65357.45  58843.40  81084.04  66198.05  58522.87  60526.98  55877.57 
##       626       627       628       629       630       631       632       633 
##  46207.26  56920.95  37465.84  37325.99  46899.90  57388.70  55432.17  84177.99 
##       634       635       636       637       638       639       640       641 
##  74358.47  76560.50  78198.75  72922.00  65637.03  62269.37  50167.28  48534.65 
##       642       643       644       645       646       647       648       649 
##  47425.58  45906.95  44290.68  46969.07  51589.14  66568.66  81001.26  78122.95 
##       650       651       652       653       654       655       656       657 
##  79027.24  84902.10  98354.49  93110.09  63363.08  60813.42  57766.05  58751.25 
##       658       659       660       661       662       663       664       665 
##  55190.17  45597.41  47983.11  52303.38  51495.12  63063.52  62941.15  63230.84 
##       666       667       668       669       670       671       672       673 
##  51679.67  53039.97  54059.01  49395.11  43371.41  46408.05  44005.48  47494.26 
##       674       675       676       677       678       679       680       681 
##  45245.41  38078.93  32733.89  32731.39  35481.95  40216.65  42478.36  40481.74 
##       682       683       684       685       686       687       688       689 
##  40505.34  40954.36  35293.53  41626.52  41123.68  41423.11  43419.73  54133.55 
##       690       691       692       693       694       695       696       697 
##  62595.65  70649.43  59940.23  55945.74  52826.32  57983.32  48270.68  41966.16 
##       698       699       700       701       702       703       704       705 
##  35858.43  32575.68  31054.80  36564.55  34827.15  35500.18  41436.47  70112.13 
##       706       707       708       709       710       711       712       713 
##  76274.18  93831.19  90148.37  92745.10 107778.69 106618.67  83967.84  84315.35 
##       714       715       716       717       718       719       720       721 
##  75648.84  72749.94  70725.35  53445.41  48841.20  52335.22  49838.63  38947.76 
##       722       723       724       725       726       727       728       729 
##  44518.81  40788.04  43034.39  40908.41  31610.58  35563.49  36189.07  29821.12 
##       730       731       732       733       734       735       736       737 
##  47898.70  50147.55  48180.40  53837.37  46239.85  46514.56  54500.29  40944.50 
##       738       739       740       741       742       743       744       745 
##  37354.58  45860.38  42633.20  44136.59  46906.76  46019.46  42436.66  49429.20 
##       746       747       748       749       750       751       752       753 
##  44447.64  65421.01  70744.08  66851.36  58782.47  78571.85  71642.49  70561.15 
##       754       755       756       757       758       759       760       761 
##  55790.66 104536.15 121615.55 126206.90 107723.24 110152.78 109399.83 107428.06 
##       762       763       764       765       766       767       768       769 
##  60081.45  45124.79  47035.00  45658.84  43634.38 152261.60 156700.73 181917.95 
##       770       771       772       773       774       775       776       777 
## 185486.27 179420.86 177417.25 184302.91  81468.38  72052.81  38410.45  41845.16 
##       778       779       780       781       782       783       784       785 
##  42254.35  46654.28  41050.26  41370.42  41212.96  45768.85  40503.47  40200.79 
##       786       787       788       789       790       791       792       793 
##  45317.33  48343.60  46597.22  43945.17  46685.25  50054.53  50460.60  45001.55 
##       794       795       796       797       798       799       800       801 
##  41035.53  41620.78  42031.09  36660.95  36742.73  36015.01  35905.63  47513.62 
##       802       803       804       805       806       807       808       809 
##  50244.20  56860.02  59009.60  53571.15  60736.88  68474.62  57339.51  50410.15 
##       810       811       812       813       814       815       816       817 
##  44259.34  48071.27  52441.61  50611.43  38614.74  36918.42  44500.08  52854.67 
##       818       819       820       821       822       823       824       825 
##  44501.58  38882.58  32585.54  43768.36  43946.92  41350.69  35519.79  44690.13 
##       826       827       828       829       830       831       832       833 
##  52738.79  57204.28  54046.14  53375.61  55939.62  59733.08  60387.25  53688.03 
##       834       835       836       837       838       839       840       841 
##  55510.21  54928.58  57174.93  60350.42  58512.75  49740.86  55196.29  50753.16 
##       842       843 
##  44496.33  48294.03 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8098
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original      bias    std. error
## t1*    5.057179   0.7816901    3.921532
## t2* 2700.830375 167.2826323  895.204321
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value  Upper CI
## 1 interrupt_var    1.059395        5.01345   13.1873
## 2    lag_depvar 1630.744280     2739.57659 4537.0444

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
    dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03")))

# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
  Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
  Data = as.numeric(mstl_data_autplt[, "Data"]),
  Trend = as.numeric(mstl_data_autplt[, "Trend"]),
  Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)

# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
  pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")

# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
  facet_wrap(~ Component, scales = "free_y", ncol = 1) +
  theme(strip.text = element_text(size = 12),
        axis.text.x = element_text(angle = 90, hjust = 1))

library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
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#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_25<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2025",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_24<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2024",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2020",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_25 %>% 
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))
Item 2025 2024 2023 2022 2021 2020
Agua 11.4882 6.993667 5.195333 5.410333 5.849167 9.93775
Comida 250.2842 326.890000 366.009167 312.386750 317.896583 392.93367
Comunicaciones 0.0000 0.000000 0.000000 0.000000 0.000000 0.00000
Electricidad 55.4514 83.582750 38.104750 47.072333 29.523000 20.60458
Enceres 2.6380 23.989000 18.259750 24.219750 14.801167 39.01200
Farmacia 0.0000 0.000000 10.704083 2.835000 13.996083 14.03675
Gas/Bencina 33.1500 44.292667 42.636000 45.575000 13.583667 17.25833
Diosi 20.8978 33.319583 55.804250 31.180667 52.687833 37.12133
donaciones/regalos 0.0000 0.000000 0.000000 0.000000 0.000000 0.00000
Electrodomésticos/ Mantención casa 0.0000 0.000000 0.000000 0.000000 0.000000 0.00000
VTR 17.5960 18.326667 12.829167 25.156667 19.086917 19.11375
Netflix 0.0000 1.391417 8.713833 7.151583 7.028750 8.24725
Otros 0.0000 76.164000 5.481667 5.000000 0.000000 0.00000
Total 391.5056 614.949750 563.738000 505.988083 474.453167 558.26542
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")

tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  54 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2746, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)
## Warning in bats(as.numeric(y), use.box.cox = use.box.cox, use.trend =
## use.trend, : optim() did not converge.
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)
# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))
## API key has been set for the current session.
try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))
## API key has been set for the current session.
# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
    dplyr::rename(ds = date3, y = value) %>%
    dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
    dplyr::mutate(unique_id = "serie_1")%>%
    dplyr::select(unique_id, ds, y)

# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
  uf_timegpt,
  h = 298,               # 298 días a pronosticar
  freq = "D",            # Frecuencia diaria
  add_history = TRUE,     # Incluir datos históricos en el output
  level = c(80,95),
  model=  "timegpt-1-long-horizon", 
  clean_ex_first = TRUE
)
## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.
# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>% 
    mutate(ds = as.Date(ds))

timegpt_fcst <- timegpt_fcst %>% 
    mutate(ds = as.Date(ds))

# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
    uf_timegpt %>% mutate(type = "Histórico"),
    timegpt_fcst %>% mutate(type = "Pronóstico")
)

# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
    # Intervalo de confianza del 95%
    geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`), 
                fill = "#4B9CD3", alpha = 0.2) +
    # Intervalo de confianza del 80%
    geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`), 
                fill = "#4B9CD3", alpha = 0.3) +
    # Línea histórica
    geom_line(data = filter(full_data, type == "Histórico"), 
              aes(color = "Histórico"), size = 1) +
    # Línea de pronóstico
    geom_line(data = filter(full_data, type == "Pronóstico"), 
              aes(color = "Pronóstico"), size = 1) +
    # Línea vertical separadora
    geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds), 
               linetype = "dashed", color = "red", size = 0.8) +
    # Configuración del eje x
    scale_x_date(
        date_breaks = "3 months",  # Reduce la frecuencia de las etiquetas
        date_labels = "%b %Y",  # Formato de etiquetas (mes y año)
    ) +
    # Configuración del eje y
    scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
    # Configuración de colores
    scale_color_manual(
        name = "Leyenda",
        values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
    ) +
    # Títulos y subtítulos
    labs(
        title = "Pronóstico de Serie Temporal con TimeGPT",
        subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
        x = "Fecha",
        y = "Valor",
        color = "Leyenda"
    ) +
    # Tema y estilos
    theme_minimal() +
    theme(
        axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.position = "bottom",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()
    )
## Warning: Removed 2746 rows containing missing values or values outside the scale range
## (`geom_line()`).

library(prophet)
## Warning: package 'prophet' was built under R version 4.4.3
## Loading required package: Rcpp
## Warning: package 'Rcpp' was built under R version 4.4.3
## Loading required package: rlang
## Warning: package 'rlang' was built under R version 4.4.3
## 
## Attaching package: 'rlang'
## The following objects are masked from 'package:purrr':
## 
##     %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
##     flatten_raw, invoke, splice
## The following object is masked from 'package:sparklyr':
## 
##     invoke
## The following object is masked from 'package:data.table':
## 
##     :=
  model <- prophet(
  cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
  # Trend flexibility
  growth = "linear",
  changepoint.prior.scale = 0.05,  # Reduced for smoother trend
  n.changepoints = 50,  # Increased from default 25
  
  # Seasonality
  yearly.seasonality = TRUE,
  weekly.seasonality = TRUE,
  daily.seasonality = FALSE,  # Disabled for daily data
  seasonality.mode = "additive",
  seasonality.prior.scale = 15,  # Increased to capture stronger seasonality
  
  # Holidays (if applicable)
  # holidays = generated_holidays  # Create with add_country_holidays()
  
  # Uncertainty intervals
  interval.width = 0.95,
  uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)
## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistent
forecast$ds <- as.Date(forecast$ds)

ggplot(forecast, aes(x = ds, y = yhat)) +
  geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper), 
              fill = "#9ecae1", alpha = 0.4) +
  geom_line(color = "#08519c", linewidth = 0.8) +
  geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
  scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
  scale_y_continuous(labels = scales::comma) +
  labs(title = "Valores predichos (95%IC)",
      # subtitle = "March 10, 2025 - May 7, 2025",
       x = "Fecha",
       y = "Valor",
      # caption = "Source: Prophet Forecast Model"
      ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(color = "gray50"),
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    plot.caption = element_text(color = "gray30")
  )

La proyección de la UF a 298 días más 2025-07-09 00:04:58 sería de: 26.580 pesos// Percentil 95% más alto proyectado: 35.072,1

Según TimeGPT: La proyección de la UF a 298 días más 2026-05-03 sería de: 40.188,82 pesos// Percentil 80% más alto proyectado: 40.557,47 pesos// Percentil 95% más alto proyectado: 41.617,67

Según prophet: La proyección de la UF a 298 días más 2026-05-03 sería de: 41.974 pesos// Percentil 95% más alto proyectado: 51.362

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 26197.33 26323.55
Lo.80 26329.23 26488.13
Point.Forecast 26580.21 26799.01
Hi.80 31419.69 32165.71
Hi.95 34328.67 35006.68


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1       mean
##       0.4299  1039.5096
## s.e.  0.1057    38.3598
## 
## sigma^2 = 37907:  log likelihood = -507.56
## AIC=1021.12   AICc=1021.45   BIC=1028.11
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1  intercept     xreg
##       0.4100   701.0985  10.4065
## s.e.  0.1073   320.5008   9.7774
## 
## sigma^2 = 37890:  log likelihood = -507.01
## AIC=1022.03   AICc=1022.59   BIC=1031.35
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 640.1200 616.8435 607.9829
Lo.80 784.9037 763.1390 701.1458
Point.Forecast 1058.4066 1039.4978 917.6171
Hi.80 1331.9094 1315.8566 1200.5224
Hi.95 1476.6931 1462.1521 1383.8652


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()
Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)

Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))

Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")

Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))

Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
  mutate(value = Tami + Andrés) %>%
  rename(ds = mes_ano, y = value) %>%
  mutate(#ds= format(ds, "%Y-%m"),
         unique_id = "1") %>% #it is only one series
  select(unique_id, ds, y)

#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
  dplyr::rename(ds = date3, y = value) %>%
  dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
  dplyr::mutate(unique_id = "serie_1")%>%
  dplyr::select(unique_id, ds, y) %>%
  mutate(ds = ymd(ds)) %>%  # Convert 'ds' to Date
  mutate(month = month(ds), year = year(ds)) %>%  # Extract month and year
  group_by(month, year) %>%  # Group by month and year
  summarise(average_y = mean(y))%>%  # Calculate average y
  mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
  ungroup()%>%
  select(ds, uf=average_y)

Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |> 
  dplyr::left_join(uf_timegpt_my, by=c("ds"="ds")) 

#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
  Gastos_casa_mensual_2022_timegpt_ex,
  h = 12,
  freq = "M",  # Monthly frequency
  add_history = TRUE,
  level = c(80, 95),
  model = "timegpt-1",#"timegpt-1-long-horizon",
  clean_ex_first = TRUE
)

# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
  mutate(ds = as.Date(paste0(ds, "-01")))  # Add day to make it a complete date

gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
  mutate(ds = as.Date(paste0(ds, "-01")))  # Add day to make it a complete date

# Combine historical and forecast data
full_data_gastos <- bind_rows(
  Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
  gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)

full_data_gastos |> 
  dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |> 
# Visualize results
ggplot(aes(x = ds, y = y)) +
  geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
              fill = "#4B9CD3", alpha = 0.2) +
  geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
              fill = "#4B9CD3", alpha = 0.3) +
  geom_line(aes(color = type), linewidth = 1.5) +
  geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
             linetype = "dashed", color = "red", linewidth = 0.8) +
  scale_x_date(
    date_breaks = "3 months",
    date_labels = "%b %Y"
  ) +
  scale_y_continuous(
    name = "Gastos Totales",
    labels = scales::comma,
    breaks = pretty(full_data_gastos$y, n = 10),
    expand = expansion(mult = c(0.05, 0.05))
  ) +
  scale_color_manual(
    name = "Leyenda",
    values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
  ) +
  labs(
    title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
    subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
    x = "Fecha",
    y = "Gastos Totales",
    color = "Leyenda"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    axis.title.x = element_text(size = 10),
    axis.title.y = element_text(size = 10),
    legend.position = "bottom",
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank()
  )


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "C:\\a\\_temp\\Library"
sessionInfo()
## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## system code page: 65001
## 
## time zone: UTC
## tzcode source: internal
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] prophet_1.0         rlang_1.1.6         Rcpp_1.0.14        
##  [4] CausalImpact_1.3.0  bsts_0.9.10         BoomSpikeSlab_1.2.6
##  [7] Boom_0.9.15         scales_1.4.0        ggiraph_0.8.13     
## [10] tidytext_0.4.2      DT_0.33             janitor_2.2.1      
## [13] autoplotly_0.1.4    rvest_1.0.4         plotly_4.10.4      
## [16] xts_0.14.1          forecast_8.24.0     wordcloud_2.6      
## [19] RColorBrewer_1.1-3  SnowballC_0.7.1     tm_0.7-16          
## [22] NLP_0.3-2           tsibble_1.1.6       lubridate_1.9.4    
## [25] forcats_1.0.0       dplyr_1.1.4         purrr_1.0.4        
## [28] tidyr_1.3.1         tibble_3.3.0        tidyverse_2.0.0    
## [31] gsynth_1.2.1        sjPlot_2.8.17       lattice_0.22-6     
## [34] GGally_2.2.1        ggplot2_3.5.2       gridExtra_2.3      
## [37] plotrix_3.8-4       sparklyr_1.9.0      httr_1.4.7         
## [40] readxl_1.4.5        zoo_1.8-14          stringr_1.5.1      
## [43] stringi_1.8.7       DataExplorer_0.8.3  data.table_1.17.4  
## [46] reshape2_1.4.4      fUnitRoots_4040.81  plyr_1.8.9         
## [49] readr_2.1.5        
## 
## loaded via a namespace (and not attached):
##   [1] bitops_1.0-9        cellranger_1.1.0    datawizard_1.1.0   
##   [4] httr2_1.1.2         lifecycle_1.0.4     StanHeaders_2.32.10
##   [7] doParallel_1.0.17   globals_0.18.0      vroom_1.6.5        
##  [10] MASS_7.3-60.2       insight_1.3.0       crosstalk_1.2.1    
##  [13] magrittr_2.0.3      sass_0.4.10         rmarkdown_2.29     
##  [16] jquerylib_0.1.4     yaml_2.3.10         fracdiff_1.5-3     
##  [19] doRNG_1.8.6.2       askpass_1.2.1       pkgbuild_1.4.8     
##  [22] DBI_1.2.3           abind_1.4-8         quadprog_1.5-8     
##  [25] nnet_7.3-19         rappdirs_0.3.3      sandwich_3.1-1     
##  [28] inline_0.3.21       data.tree_1.1.0     tokenizers_0.3.0   
##  [31] listenv_0.9.1       anytime_0.3.11      performance_0.14.0 
##  [34] spatial_7.3-17      parallelly_1.45.0   codetools_0.2-20   
##  [37] xml2_1.3.8          tidyselect_1.2.1    ggeffects_2.2.1    
##  [40] farver_2.1.2        urca_1.3-4          its.analysis_1.6.0 
##  [43] matrixStats_1.5.0   stats4_4.4.0        jsonlite_2.0.0     
##  [46] ellipsis_0.3.2      Formula_1.2-5       iterators_1.0.14   
##  [49] systemfonts_1.2.3   foreach_1.5.2       tools_4.4.0        
##  [52] glue_1.8.0          xfun_0.52           TTR_0.24.4         
##  [55] ggfortify_0.4.17    loo_2.8.0           withr_3.0.2        
##  [58] timeSeries_4041.111 fastmap_1.2.0       boot_1.3-30        
##  [61] openssl_2.3.3       caTools_1.18.3      digest_0.6.37      
##  [64] timechange_0.3.0    R6_2.6.1            lfe_3.1.1          
##  [67] colorspace_2.1-1    networkD3_0.4.1     gtools_3.9.5       
##  [70] generics_0.1.4      htmlwidgets_1.6.4   ggstats_0.9.0      
##  [73] pkgconfig_2.0.3     gtable_0.3.6        timeDate_4041.110  
##  [76] lmtest_0.9-40       selectr_0.4-2       janeaustenr_1.0.0  
##  [79] htmltools_0.5.8.1   carData_3.0-5       tseries_0.10-58    
##  [82] snakecase_0.11.1    knitr_1.50          rstudioapi_0.17.1  
##  [85] tzdb_0.5.0          uuid_1.2-1          nlme_3.1-164       
##  [88] curl_6.3.0          cachem_1.1.0        sjlabelled_1.2.0   
##  [91] KernSmooth_2.23-22  parallel_4.4.0      fBasics_4041.97    
##  [94] pillar_1.10.2       vctrs_0.6.5         gplots_3.2.0       
##  [97] slam_0.1-55         car_3.1-3           dbplyr_2.5.0       
## [100] xtable_1.8-4        evaluate_1.0.3      mvtnorm_1.3-3      
## [103] cli_3.6.5           compiler_4.4.0      crayon_1.5.3       
## [106] rngtools_1.5.2      future.apply_1.20.0 labeling_0.4.3     
## [109] sjmisc_2.8.10       rstan_2.32.7        QuickJSR_1.7.0     
## [112] viridisLite_0.4.2   assertthat_0.2.1    lazyeval_0.2.2     
## [115] Matrix_1.7-0        sjstats_0.19.0      hms_1.1.3          
## [118] bit64_4.6.0-1       future_1.58.0       nixtlar_0.6.2      
## [121] extraDistr_1.10.0   igraph_2.1.4        RcppParallel_5.1.10
## [124] bslib_0.9.0         quantmod_0.4.27     bit_4.6.0
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))